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Review
. 2024 Mar 7:18:1363128.
doi: 10.3389/fnins.2024.1363128. eCollection 2024.

Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Marta Carè  1 , Michela Chiappalone  2   3 , Vinícius Rosa Cota  3
Affiliations
Review

Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Marta Carè et al. Front Neurosci. .

Abstract

Despite considerable advancement of first choice treatment (pharmacological, physical therapy, etc.) over many decades, neurological disorders still represent a major portion of the worldwide disease burden. Particularly concerning, the trend is that this scenario will worsen given an ever expanding and aging population. The many different methods of brain stimulation (electrical, magnetic, etc.) are, on the other hand, one of the most promising alternatives to mitigate the suffering of patients and families when conventional treatment fall short of delivering efficacious treatment. With applications in virtually all neurological conditions, neurostimulation has seen considerable success in providing relief of symptoms. On the other hand, a large variability of therapeutic outcomes has also been observed, particularly in the usage of non-invasive brain stimulation (NIBS) modalities. Borrowing inspiration and concepts from its pharmacological counterpart and empowered by unprecedented neurotechnological advancement, the neurostimulation field has seen in recent years a widespread of methods aimed at the personalization of its parameters, based on biomarkers of the individuals being treated. The rationale is that, by taking into account important factors influencing the outcome, personalized stimulation can yield a much-improved therapy. Here, we review the literature to delineate the state-of-the-art of personalized stimulation, while also considering the important aspects of the type of informing parameter (anatomy, function, hybrid), invasiveness, and level of development (pre-clinical experimentation versus clinical trials). Moreover, by reviewing relevant literature on closed loop neuroengineering solutions in general and on activity dependent stimulation method in particular, we put forward the idea that improved personalization may be achieved when the method is able to track in real time brain dynamics and adjust its stimulation parameters accordingly. We conclude that such approaches have great potential of promoting the recovery of lost functions and enhance the quality of life for patients.

Keywords: activity-dependent; anatomical; functional; humans; in-vivo; individualized; neurodynamics.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor FT declared a past collaboration with the author(s) MC and VC. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
Personalized stimulation: from techniques to applications. (Left) Typical sources used to record target areas or patterns of interest are anatomical and/or functional. (Top, right) Electroceutical therapy typically involves the use of electrical stimulation to modulate neural activity in the nervous system. The application of electrical stimulation can be invasive (blueish hues), typically performed in animal models in vivo (e.g., rodents and primates) or non-invasive (reddish hues), most commonly done in humans. (Bottom, right) Neurostimulation can be used for various purposes, including pain management, treatment of neurological conditions, and rehabilitation.
Figure 2
Figure 2
Obtaining individual neurodynamics. A general schematic flow of how to obtain/localize the source within the area of interest for each subject. (Top) Selection of target data (e.g., structural brain MRI, individual frequency, fMRI). (Middle) The personalized target selection is computationally elaborated (e.g., SofTaxic Neuronavigation, FSS, Hilbert transform, SMA activation analysis) in order to guide the procedure (e.g., stereotaxic, individualized frequency) for the personalization (e.g., electrode’s shape, localization/place). (Bottom) The personalized target is identified for stimulation.
Figure 3
Figure 3
Open- and closed-loop architectures. (A) In the standard open-loop modality (top left), the delivered stimuli are not correlated to the network activity. It is possible to personalize the open-loop approach by designing a stimulation pattern, which reproduces the intrinsic dynamics of a target (bottom left). (B) On the contrary, closed-loop configurations rely on feedback. The signals from the network undergo processing, and specific features are extracted. Consequently, triggering events are generated, responsible for delivering stimulation pulses in accordance with the current network state.
Figure 4
Figure 4
Overview of the ADS stimulation paradigm. A stimulation trial, during which a single-unit activity was detected on a single channel within the premotor cortex. The latency between the spike detection in the trigger area and delivery of a stimulus pulse was set at 10 ms (2.5 ms spike processing time, 7.5 ms imposed delay). To prohibit stimulus-activated spikes and stimulus artifacts from triggering stimulation, a short blanking period (28 ms) followed each stimulus. This activity was used to trigger a single stimulation pulse in the stimulation area [i.e., S1 (Guggenmos et al., 2013) or the ventral horn of the thoracic spinal cord below the level of the injury (Borrell et al., 2020)] in order to induce changes in the strength of the resultant activity through Hebbian mechanisms. A stimulus was triggered each time a user-selected neuronal spike profile was recorded from a single recording site in the motor cortex.
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Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) – A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553(DN. 11.10.2022)). VC was supported by the Marie Skłodowska-Curie Individual Fellowship MoRPHEUS, Grant Agreement No. 101032054, funded by the European Union under the framework programme H2020-EU.1.3.-EXCELLENT SCIENCE.

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